Theory of cortical function

Significance

A unified theory of cortical function is proposed for guiding both neuroscience and artificial intelligence research. The theory offers an empirically testable framework for understanding how the brain accomplishes three key functions: (i) inference: perception is nonconvex optimization that combines sensory input with prior expectation; (ii) exploration: inference relies on neural response variability to explore different possible interpretations; (iii) prediction: inference includes making predictions over a hierarchy of timescales. These three functions are implemented in a recurrent and recursive neural network, providing a role for feedback connections in cortex, and controlled by state parameters hypothesized to correspond to neuromodulators and oscillatory activity.

Abstract

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.

Perception is an unconscious inference (1). Sensory stimuli are inherently ambiguous so there are multiple (often infinite) possible interpretations of a sensory stimulus (Fig. 1). People usually report a single interpretation, based on priors and expectations that have been learned through development and/or instantiated through evolution. For example, the image in Fig. 1A is unrecognizable if you have never seen it before. However, it is readily identifiable once you have been told that it is an image of a Dalmatian sniffing the ground near the base of a tree. Perception has been hypothesized, consequently, to be akin to Bayesian inference, which combines sensory input (the likelihood of a perceptual interpretation given the noisy and uncertain sensory input) with a prior or expectation (2–5).

Fig. 1.

Perceptual inference. (A) Prior expectation. Reprinted with permission from ref. 84. (B) Perceptual multistability. Reprinted with permission from ref. 85.

Our brains explore alternative possible interpretations of a sensory stimulus, in an attempt to find an interpretation that best explains the sensory stimulus. This process of exploration happens unconsciously but can be revealed by multistable sensory stimuli (e.g., Fig. 1B), for which one’s percept changes over time. Other examples of bistable or multistable perceptual phenomena include binocular rivalry, motion-induced blindness, the Necker cube, and Rubin’s face/vase figure (6). Models of perceptual multistability posit that variability of neural activity contributes to the process of exploring different possible interpretations (e.g., refs. 7–9), and empirical results support the idea that perception is a form of probabilistic sampling from a statistical distribution of possible percepts (9, 10). This noise-driven process of exploration is presumably always taking place. We experience a stable percept most of the time because there is a single interpretation that is best (a global minimum) with respect to the sensory input and the prior. However, in some cases, there are two or more interpretations that are roughly equally good (local minima) for bistable or multistable perceptual phenomena (9, 11, 12).

Prediction, along with inference and exploration, may be a third general principle of cortical function. Information processing in the brain is dynamic. Visual perception, for example, occurs in both space and time. Visual signals from the environment enter our eyes as a continuous stream of information, which the brain must process in an ongoing, dynamic way. How we perceive each stimulus depends on preceding stimuli and impacts our processing of subsequent stimuli. Most computational models of vision are, however, static; they deal with stimuli that are isolated in time or at best with instantaneous changes in a stimulus (e.g., motion velocity). Dynamic and predictive processing is needed to control behavior in sync with or in advance of changes in the environment. Without prediction, behavioral responses to environmental events will always be too late because of the lag or latency in sensory and motor processing. Prediction is a key component of theories of motor control and in explanations of how an organism discounts sensory input caused by its own behavior (e.g., refs. 13–15). Prediction has also been hypothesized to be essential in sensory and perceptual processing (16–18). However, there is a paucity of theories for how the brain performs perceptual predictions over time (19–23), noting that many of the so-called “predictive coding theories” of sensory and perceptual processing do not predict forward in time and are not in line with physiological and psychological phenomena (Discussion). Moreover, prediction might be critical for yet a fourth general principle of cortical function: learning (Discussion).

The neocortex accomplishes these functions (inference, exploration, prediction) using a modular design with modular circuits and modular computations. Anatomical evidence suggests the existence of canonical microcircuits that are replicated across cortical areas (24, 25). It has been hypothesized, consequently, that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations of the same form, hierarchically (e.g., refs. 26 and 27). Most models of sensory processing in the brain, and many artificial neural nets (called deep convolutional neural nets), have a feedforward architecture in which each stage comprises a bank of linear filters followed by an output nonlinearity (Fig. 2 A and B). These hierarchical, feedforward processing models have served us well. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence are based on this architecture. However, neocortex is not a feedforward architecture. There is compelling evidence for a number of distinct, interconnected cortical areas (e.g., 30 or so in visual cortex), but for every feedforward connection there is a corresponding feedback connection, and there is little or no consensus about the function(s) of these feedback connections (28).

Fig. 2.

Neural net architecture and computation. (A) Feedforward architecture. Each box represents a channel, comprising a large number of neurons (small circles). All neurons in a channel perform the same computation, with shifted copies of the same weights. (B) Neural computation module in the feedforward network. Each neuron computes a weighted sum of its inputs, followed by a squaring output nonlinearity. (C) Recurrent architecture. (D) Neural computation module in the recurrent network. Feedforward weights (same as B) drive the neuron’s response to be the same as A, but this feedforward drive competes with prior drive and feedback drive. Dashed line, the prior can be computed recursively over time.

Perceptual phenomena also suggest a role for feedback in cortical processing. For example, memory contributes to what we perceive. Take another look at the Dalmatian image (Fig. 1A); then close your eyes and try to visualize the image. This form of memory recall (called visual imagery or mental imagery) generates patterns of activity in visual cortex that are similar to sensory stimulation (e.g., ref. 29). One way to conceptualize visual imagery is to think of it as an extreme case of inference that relies entirely on a prior/expectation with no weight given to the sensory input.

This paper represents an attempt toward developing a unified theory of cortical function, an empirically testable computational framework for guiding both neuroscience research and the design of machine-learning algorithms with artificial neural networks. It is a conceptual theory that characterizes computations and algorithms, not the underlying circuit, cellular, molecular, and biophysical mechanisms (Discussion). According to the theory, neural activity in each brain area depends on feedforward drive (bottom-up from a previous stage in the processing hierarchy), feedback drive (top-down context from a subsequent processing stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters. The theory makes explicit how information is processed continuously through time to perform inference, exploration, and prediction. Although I focus on sensation and perception (specifically vision), I hypothesize that the same computational framework applies throughout neocortex.

The computational framework presented here, of course, includes components previously proposed in computational/theoretical neuroscience, image processing, computer vision, statistics, and machine learning with artificial neural networks (SI Appendix). I was particularly influenced by an underappreciated signal-processing paper by José Marroquin et al. (30).

Results

In a typical feedforward model of visual processing, the underlying selectivity of each neuron is hypothesized to depend on a weighted sum of its inputs, followed by an output nonlinearity (Fig. 2 A and B). The weights (which can be positive or negative) differ across neurons conferring preferences for different stimulus features. For neurons in primary visual cortex (V1), for example, the choice of weights determines the neuron’s selectivity for orientation, spatial frequency, binocular disparity (by including inputs from both eyes), etc. Taken together, neurons that have the same weights, but shifted to different spatial locations, are called a “channel” (also called a “feature map” in the neural net literature). The responses of all of the neurons in a channel are computed as a convolution over space (i.e., weighted sums at each spatial position) with spatial arrays of inputs from channels in the previous stage in the processing hierarchy, followed by the output nonlinearity. The examples in this paper, only for the sake of simplicity, used quadratic output nonlinearities, but a computation called “the normalization model” has been found to be a better model (both theoretically and empirically) of the output nonlinearity (refs. 31 and 32; SI Appendix). Neurons in each successive stage of visual processing have been proposed to perform the same computations. According to this idea, each layer 2 neuron computes a weighted sum of the responses of a subpopulation of layer 1 neurons, and then the response of each layer 2 neuron is a nonlinear function of the weighted sum. (I am using the term “layer” to refer to subsequent stages of processing, following the terminology used in the neural network literature, not intended to map onto the layered anatomical structure of the cortex within a brain area.)

Here, I take a different approach from the feedforward processing model, and instead propose a recurrent network (Fig. 2 C and D). Similar to the feedforward network, there is again a hierarchy of processing stages, each comprising a number of channels. Also similar to the feedforward network, all neurons in a channel perform the same computation, with shifted copies of the same weights, and an output nonlinearity. However, in addition, the network includes a feedback connection for every feedforward connection (Fig. 2C, two-sided arrows, and Fig. 2D). Each neuron also has another input that I call a prior, which can be either prespecified or computed recursively (Fig. 2D). The response of each neuron is updated over time by summing contributions from the three inputs: feedforward drive, feedback drive, and prior drive (Fig. 2D). Each neuron also provides two outputs: feedforward drive to the next layer, and feedback drive to the previous layer. Each neuron performs this computation locally, based on its inputs at each instant in time. However, the responses of the full population of neurons (across all channels and all layers) converge to minimize a global optimization criterion, which I call an energy function. First, I define the energy function. Then, I derive (simply by taking derivatives with the chain rule) how each neuron’s responses are updated over time.

The starting point is the hypothesis that neural responses minimize an energy function that represents a compromise between the feedforward drive and prior drive (see Table 1 for a summary of notation):

$$E={\displaystyle \sum _{i=1}^L{\alpha }^{\left(i\right)}\left[{\lambda }^{\left(i\right)}{\displaystyle \sum _j{\left(f_j^{\left(i\right)}\right)}^2}+\left(1-{\lambda }^{\left(i\right)}\right){\displaystyle \sum _j{\left(p_j^{\left(i\right)}\right)}^2}\right]},$$ [1]

$$f_j^{\left(i\right)}=y_j^{\left(i\right)}-z_j^{\left(i\right)}\hspace{0.17em}\left(\text{feedforward}\hspace{0.17em}\text{drive}\right),$$

$$p_j^{\left(i\right)}=y_j^{\left(i\right)}-{\widehat{y}}_j^{\left(i\right)}\hspace{0.17em}\left(\text{prior}\hspace{0.17em}\text{drive}\right),$$

$$z_j^{\left(i\right)}={\left(v_j^{\left(i\right)}\right)}^2\hspace{0.17em}\left(\text{quadratic}\hspace{0.17em}\text{output}\hspace{0.17em}\text{nonlinearity}\right),$$

$$v_j^{\left(i\right)}={\displaystyle \sum _k{w}_{jk}^{\left(i-1\right)}{y}_k^{\left(i-1\right)}}\hspace{0.17em}\left(\text{weighted}\hspace{0.17em}\text{sum}\right).$$

The variables (y, v, z, and ŷ) are each functions of time; I have omitted time in this equation to simplify the notation, but I deal with time and dynamics below.

Table 1.

Notation for Eqs. 1 and 2

Symbol	Description
$y_j^{\left(i\right)}\left(t\right)$	Responses over time of the $j$th neuron in layer $\left(i\right)$
$y_j^{\left(0\right)}\left(t\right)$	Inputs (layer 0)
${\widehat{y}}_j^{\left(i\right)}\left(t\right)$	Prior expectation (target values) for the responses of the $j$th neuron in layer $\left(i\right)$
$w_{jk}^{\left(i-1\right)}$	Weights from neuron $k$ in layer $\left(i\right)$ − 1 to neuron $j$ in layer $\left(i\right)$
$v_j^{\left(i\right)}\left(t\right)$	Weighted sum of the responses from the previous layer
$z_j^{\left(i\right)}\left(t\right)$	Weighted sum followed by quadratic output nonlinearity
$f_j^{\left(i\right)}\left(t\right)$	Feedforward drive for the $j$th neuron in layer $\left(i\right)$
$p_j^{\left(i\right)}\left(t\right)$	Prior drive for the $j$th neuron in layer$\left(i\right)$
$b_j^{\left(i\right)}\left(t\right)$	Feedback drive for the $j$th neuron in layer $\left(i\right)$. See Eq. 2
$\alpha \left(t\right),\lambda \left(t\right)$	State parameters

The values of y are the responses (proportional to firing rates) of the neurons in each layer of the network, where y⁽⁰⁾ (layer 0) comprises the inputs to the multilayer hierarchy. The superscript (i) specifies the layer in the hierarchy. For example, layer 1 might correspond to neurons in the lateral geniculate nucleus (LGN) of the thalamus, which receives inputs from the retina (noting that there is no feedback to the retina), and layer 2 might correspond to neurons in V1 that receive direct inputs from the LGN, etc.

The first term of E drives the neural responses to explain the input from the previous layer; f is called the feedforward drive (Eq. 1, second line). With only this term, the neural responses would be the same as those in a purely feedforward model. The values of v are weighted sums of the responses from the previous layer, and w are the weights in those weighted sums (Eq. 1, fifth line). The weights are presumed to be the same for all neurons in a channel, but shifted to different spatial locations (i.e., the values of v can be computed with convolution over space). The values of z determine the feedforward drive, after the quadratic output nonlinearity (Eq. 1, fourth line).

The second term of E drives the neural responses to match a prior; p is called the prior drive (Eq. 1, third line). With only this term, the neural responses would be driven to be the same as the values of ŷ. The values of ŷ might, for example, be drawn from memory and propagated via the feedback drive to a sensory representation (as detailed below), and/or used to predict forward in time (also detailed below). I show that the values of ŷ can be interpreted as an implicit representation of a prior probability distribution (see Bayesian Inference: Cue Combination and SI Appendix), so I use the term “prior” when referring to ŷ. For some of the examples, however, it is more appropriate to think of ŷ as target values for the responses. For other examples, the values of ŷ can be interpreted as predictions for the responses. (I see it as a feature that the various components of the theory can be interpreted in different ways to connect with different aspects of the literature.)

The α and λ (0 < λ < 1) are state parameters, which I hypothesize change over time under control of other brain systems (Discussion and SI Appendix). The values of α determine the relative contribution of each layer to the overall energy, and the values of λ determine the trade-off between the two terms in the energy function at each layer. Changing the values of α and λ, as demonstrated below, changes the state of the neural network. With only the first term (i.e., λ = 1), for example, the neural responses are determined by the feedforward drive, and the network behaves exactly like a conventional feedforward network. With only the second term (i.e., λ = 0), the neural responses follow the prior and completely ignore the sensory inputs.

For simplicity, Eq. 1 denotes a network with only one channel in each layer, but it can easily be extended to have multiple channels per layer (SI Appendix). It is a global optimization criterion; the summation is over all neurons in all channels and all layers, and a summation over time can also be included (see Prediction and SI Appendix).

The neural responses are modeled as dynamical processes that minimize the energy E over time. Taking derivatives of Eq. 1 (using the chain rule):

$$\tau \frac{d{y}_j^{\left(i\right)}}{dt}=-\frac{dE}{d{y}_j^{\left(i\right)}}=-2{\alpha }^{\left(i\right)}{\lambda }^{\left(i\right)}{f}_j^{\left(i\right)}+4{\alpha }^{\left(i+1\right)}{\lambda }^{\left(i+1\right)}{b}_j^{\left(i\right)}-2{\alpha }^{\left(i\right)}\left(1-{\lambda }^{\left(i\right)}\right)p_j^{\left(i\right)},$$ [2]

$$b_j^{\left(i\right)}={\displaystyle \sum _k\left[y_k^{\left(i+1\right)}-z_k^{\left(i+1\right)}\right]v_k^{\left(i+1\right)}{w}_{kj}^{\left(i\right)}}\hspace{0.17em}\left(\text{feedback}\hspace{0.17em}\text{drive}\right).$$

According to this equation, neural responses are updated over time because of a combination of feedforward drive f, feedback drive b, and prior drive p. The first term f is the same feedforward drive as above, and the third term p is the same prior drive as above. The middle term b, the feedback drive, is new. The feedback drive drops out when taking the derivative of Eq. 1 because the response of each neuron appears twice in that equation: (i) the derivative of [y_j⁽ⁱ⁾ − z_j⁽ⁱ⁾]² gives the feedforward drive; (ii) the derivative of [y_k⁽ⁱ⁺¹⁾ − z_k⁽ⁱ⁺¹⁾]² gives the feedback drive because z_k⁽ⁱ⁺¹⁾ depends on y_j⁽ⁱ⁾ (SI Appendix). The prior drive contributes to minimizing the second term of E in Eq. 1. The feedforward drive and the feedback drive both contribute to minimizing the first term of E in Eq. 1. The combined effect of the feedforward drive and the feedback drive is that if the response of a neuron is larger than the value provided by the feedforward processing of its inputs, then its response gets tamped down and its inputs get cranked up; or vice versa if the response of a neuron is smaller than the feedforward value. Specifically, the feedback to layer (i) depends on the mismatch between the responses in the next layer (i + 1) and the feedforward drive from layer (i) to layer (i + 1); this mismatch is then transformed back to layer (i) through the transpose of the weight matrix (SI Appendix). The value of τ is a time constant that I interpret as a combination of the time constant of a neuron’s cell membrane and the time constant of synaptic integration.

Inference.

Depending on the state parameters (the values of α and λ at each layer), the responses are dominated by sensory input, prior expectation, or a combination of the two.

As a simple example, a three-layer network was implemented that computed a cascade of exclusive-or (XOR) operations (Fig. 3A). The response of the layer 3 neuron was 1 if the inputs at layer 0 consisted of a single 1 with three 0s or a single 0 with three 1s. The feedforward drive of each neuron was equal to the square of the difference between its inputs: (0 − 0)² = 0, (0 − 1)² = 1, (1 − 0)² = 1, (1 − 1)² = 0. The weights (−1, 1) were the same for each neuron.

Fig. 3.

Inference. (A) Network architecture. Feedforward drive of each neuron is the square of the difference between its two inputs. The two examples correspond to responses in B and C. (B) Driven by sensory input. Each panel corresponds to a layer. Each curve corresponds to the response time course of a neuron. Colors correspond to A, Left. Input: y⁽⁰⁾ = (1, 0, 0, 0). Prior: ŷ = 0 for all neurons in the network. State: λ = (1, 1, 1) and α = (1,0.1,0.1), for layers 1, 2, and 3, respectively. (C) Driven by memory. Colors correspond to A, Right. Input: y⁽⁰⁾ = (0,0,0,0). Prior: ŷ = 1 for the layer 3 neuron and ŷ = 0 for all other neurons in the network. State: λ = (1, 1, 0.1) and α = (0.001, 0.1, 1). Time constant: τ = 5 ms. See SI Appendix for details.

The network behaved like a feedforward model for some values of the state parameters (Fig. 3B). Responses of the four neurons in layer 1 rapidly converged to values matching the input (Fig. 3B, bottom panel). Responses of the two neurons in layer 2 and the neuron in layer 3 each converged more slowly to values determined by the feedforward drive (Fig. 3B, top two panels). Because of the choice of state parameters, the energy was dominated by the feedforward drive (Eq. 1, first term) in layer 1, whereas the prior (Eq. 1, second term) was ignored in all three layers.

The network behaved like a simple example of memory recall or visual imagery for other values of the state parameters (Fig. 3C). The state parameters were set to values so that the energy function (Eq. 1) was dominated by the layer 3 prior. Consequently, the response of the layer 3 neuron converged to a value determined by its prior (Fig. 3C, top panel). The responses of the neurons in layers 2 and 1 converged more slowly to values that were consistent with the layer 3 prior (Fig. 3C, bottom two panels). Hence, the value of the prior for the layer 3 neuron propagated back to generate or reconstruct a representation in layer 1. This reconstructed representation in layer 1 corresponded to a sensory input that would have evoked, in a feedforward network, the same layer 3 response. The reconstruction emerged over time; the rise in the neural responses were delayed by ∼100 ms in layer 2 relative to layer 3, and in layer 1 relative to layer 2, even though the time constant was short (τ = 5 ms). Rerunning the simulation yielded different results, depending on the initial conditions (i.e., different initial values for the responses for each of the neurons). However, in all cases, the responses of the layer 1 neurons converged to values that were consistent with the layer 3 prior (i.e., a single 1 with three 0s or a single 0 with three 1s). The layer 3 prior was ambiguous; it did not reconstruct a specific memory but rather a class of memories because there were multiple local minima in the energy function: any input consisting of a single 1 with three 0s or a single 0 with three 1s was consistent with setting the layer 3 prior to 1.

When presented with an ambiguous sensory input, the network was biased by a prior, analogous to the Dalmatian image (Fig. 1A). For example, when the input was specified to be (0.5, 0, 0, 0), and the prior for the layer 3 neuron was set to 1, then the global minimum energy state corresponded to responses of the layer 1 neurons of approximately (1, 0, 0, 0). Alternatively, when the prior for the layer 3 neuron was set to 0, then the responses of the layer 1 neurons converged to (0, 0, 0, 0). The sensory input was the same and the state parameters were the same, but the network converged to a different solution, depending on the layer 3 prior.

Exploration.

The network explored different possible perceptual interpretations (exhibiting bistability, analogous to Fig. 1B) when the input and prior were inconsistent with one another (Fig. 4). The input was specified to be (1, 0, 1, 0) and the prior for the layer 3 neuron was set to 1, such that the inputs were incompatible with the layer 3 prior. Bistability emerged by adding noise to the neural responses. The layer 1 responses remained close to the inputs (Fig. 4, bottom panel) and the response of the layer 3 neuron remained close to its prior (Fig. 4, top panel). However, the responses of the layer 2 neurons changed over time, alternating between (1, 0) and (0, 1), which corresponded to two local minima in the energy function. The noise was statistically independent across neurons and over time, but nonstationary. In particular, the time course of the SD had a 1/f amplitude spectrum (Discussion and SI Appendix), but similar results were obtained when the SD modulated periodically (e.g., at 10 Hz) instead of having a 1/f amplitude spectrum, or when the noise SD was a sum of periodic and 1/f components.

Fig. 4.

Exploration. Responses in layer 2 exhibit bistability (same format as Fig. 3 B and C). Input: y⁽⁰⁾ = (1, 0, 1, 0). Prior: ŷ = 1 for the layer 3 neuron and ŷ = 0 for all other neurons in the network. State: λ = (1, 1, 0.1) and α = (0.1, 0.1, 1), for layers 1, 2, and 3, respectively. Time constant: τ = 10 ms. See SI Appendix for details.

Bayesian Inference: Cue Combination.

These principles, inference based on prior expectation (often formalized as Bayesian inference) and exploration of alternative possible interpretations (hypothesized to be driven by neural response variability), apply not only to perception but also to motor control, motor learning, and cognition (e.g., refs. 33–38). Consequently, there is considerable interest in how neural populations can represent uncertainty and priors, and perform probabilistic inference and probabilistic learning (2–4, 10, 39–44).

The two terms of Eq. 1 are analogous to Bayesian inference, with the first term representing a negative log likelihood and the second term representing a prior probability. Following previous work on probabilistic population codes (4, 21, 40), the idea is that the neural responses encode an implicit representation of the posterior. Indeed, the values of ŷ can be interpreted as an implicit representation of a prior probability distribution, and the values of y can be interpreted as an implicit representation of the posterior (SI Appendix). The quadratic function in the first term of Eq. 1 corresponds to a normal distribution for the noise in the feedforward drive and the quadratic in the second term determines the prior probability distribution. Different cost functions (other than quadratics) would correspond to different statistical models of the noise and prior (e.g., refs. 45 and 46). I cannot claim that the theory is, in general, Bayesian, but there are special cases that approximate Bayesian inference.

To make explicit the link to Bayesian inference, I use cue combination as an example. In a cue combination task, an observer is presented with two or more sources of information (cues) about a perceptual variable. For example, early empirical work on cue combination used two depth cues (e.g., stereo and motion parallax) (47). One of the cues is typically more reliable than the other, and the reliability of both cues may vary from one trial to the next of the experiment (e.g., by varying the contrast or visibility of one or both cues). Both cues may be consistent with the same interpretation or they may be in conflict, such that each cue supports a slightly different interpretation. Observers in such an experiment are instructed to indicate their percept (e.g., depth estimate). A series of studies have reported that percepts depend on a combination of the two cues, the reliability of both cues, and a prior. Cue combination tasks have, consequently, been formalized as Bayesian estimation (47), and some empirical results suggest that cue combination is approximately Bayes-optimal (e.g., refs. 5, 48, and 49). The broader literature on psychophysics and perceptual decision-making can be encompassed by this same formalism, with only one cue instead of two.

I implemented a network that combines information from two sensory cues with a prior to simulate a cue combination experiment; the network was designed to approximate optimal Bayesian cue combination. The network consisted of a layer of output neurons and two sets of input neurons (Fig. 5A). Each of the input neurons was tuned for depth, responding most strongly to a preferred depth value (Fig. 5B). Both sets of input neurons had the same tuning curves but responded to each of two different cues (e.g., stereo and motion parallax). The stimulus strength of each of the two cues scaled the gain of the input neuron’s responses, and the input neuron’s responses were presumed to be noisy (additive, independent, normally distributed noise). The feedforward drive for each output neuron was a weighted sum of the two input neurons with the corresponding tuning curve (SI Appendix), so the output neurons had the same tuning curves as the input neurons. A target value ŷ was specified for the response of each output neuron. These target values could be learned, for example, as the mean response of each output neuron, averaged across a series of practice/training trials. These target values for the responses corresponded to a prior probability distribution over the stimulus depth values; each neuron responded selectively to a preferred stimulus depth so a large target value for a particular neuron meant that the corresponding stimulus depth was more likely. Consequently, the vector of ŷ values can be transformed to a function that is proportional to a prior probability distribution (Fig. 5C; SI Appendix). I also defined a readout rule (SI Appendix) that transformed the vector of responses of the output neurons to a depth estimate (Fig. 5D, approximately equal to the mean of the posterior) and an uncertainty (Fig. 5E, approximately equal to the SD of the posterior).

Fig. 5.

Bayesian estimation. (A) Network architecture. Top row, each circle corresponds to one of 23 output neurons, each tuned for depth (B). Bottom row, each circle corresponds to an input neuron. The two sets of input neurons respond to two different cues (e.g., stereo and motion parallax). (B) Tuning curves. Each input neuron responds preferentially to a range of depth values with a raised-cosine tuning curve. The tuning curve for the jth neuron is denoted ψ_j(s), where s is the stimulus depth. (C) Example prior probability distributions. Blue, prior corresponding to ŷ_j⁽¹⁾ = ψ_j(0) with uncertainty σ₀ = 0.5. Orange, prior corresponding to ŷ_j⁽¹⁾ = ψ_j(-0.5) + ψ_j(0.5) with uncertainty σ₀ = 0.25. (D) Depth estimates. Solid curves, optimal Bayesian estimation. Circles, cue combination network. In the absence of sensory input, the most likely depth was 0 (“prior”). The two sensory cues indicated different depth values (“cue 1” and “cue 2”). Stimulus strengths are specified in units of detectability (d′), where d′ = 1 corresponds to a stimulus that is barely detectable. (E) Uncertainty. Solid curves, optimal Bayesian. Circles, cue combination network. Simulation parameters: cue 1 indicated s₁ = 0.1; cue indicated s₂ = 0.2; reliability of cue 2 twice that of cue 1 (i.e., σ₁ = 2, σ₂ = 1); prior corresponded to blue curve in C. See SI Appendix for details.

Depth estimates and uncertainties computed with this readout from the network were strongly correlated with optimal Bayesian estimates and uncertainties (estimates: r = 0.94; uncertainties: r = 0.98). The network was a particularly good approximation to Bayesian inference in two regimes of stimulus strengths (see SI Appendix for derivation). (i) When the stimulus strength of one or both cues was large, depth estimates and uncertainties depended on the relative reliabilities of the two cues (Fig. 5D, Top, Right, and Top Right; Fig. 5E, Bottom, Right, and Bottom Right). (ii) When the stimulus strengths of both cues were small, depth estimates and uncertainties were dominated by the prior (Fig. 5D, Bottom Left; Fig. 5E, Top Left). This network illustrates how the general framework I have laid out can be used to solve fairly complex probabilistic inference near optimally, but it remains to be seen whether this particular model of multisensory integration can account for the experimental data to the same extent as other theories such as the linear probabilistic population code (4, 49).

I am not suggesting that the prior probability distribution (plotted in Fig. 5C) and readout (plotted in Fig. 5 D and E) are explicitly computed and represented in the brain. Rather, the vector of target values ŷ (that implicitly encodes a prior) in one channel/layer interacts with the inputs to evoke a vector of neural responses y (that implicitly encodes a posterior). Neural responses in one channel/layer interact (through feedforward and feedback drive) with neural responses in other channels/layers (that implicitly encode their corresponding posteriors), to yield neural responses in all channels and layers that correspond to a globally optimal inference.

Prediction.

Prediction requires a model. The idea here is to rely on the generative model embedded in the hierarchical neural network, coupled with the intuition that the relevant timescales are different at each level of the hierarchy (50). The sensory inputs at the bottom of the hierarchy change rapidly but the more abstract representations at successively higher levels change more slowly over time. A simple example is a network in which the responses in one layer are sinusoidal and the feedforward drive to the next layer computes the sum of squares of a pair of neurons that respond with temporal phases offset by 90° (e.g., sine- and cosine-phase). The sinusoids modulate rapidly over time, but the sum of squares is constant over time. The responses of each neuron in the network are computed and predicted recursively over time, for example, with recurrent excitation and inhibition within each module of each channel (Fig. 2D, dashed line), and the values of ŷ can be interpreted as predictions for the responses. Slow changes at higher layers constrain, via the feedback drive, predictions at lower layers.

The energy function for a one-layer prediction network is expressed as follows (see Table 2 for a summary of notation):

$$E={\displaystyle \sum _t\lambda \left(t\right){\left[\left({\displaystyle \sum _m{y}_{m_1}^{\left(1\right)}\left(t\right)}\right)-y^{\left(0\right)}\left(t\right)\right]}^2}+{\displaystyle \sum _t\left(1-\lambda \left(t\right)\right)\left[{\displaystyle \sum _m{\left(y_{m_1}^{\left(1\right)}\left(t\right)-{\widehat{y}}_{m_1}^{\left(1\right)}\left(t\right)\right)}^2+{\left(y_{m_2}^{\left(1\right)}\left(t\right)-{\widehat{y}}_{m_2}^{\left(1\right)}\left(t\right)\right)}^2}\right]},$$ [3]

$$\begin{array}{l}{\widehat{y}}_{m_1}^{\left(1\right)}\left(t\right)=y_{m_1}^{\left(1\right)}\left(t-\mathrm{\Delta }t\right)\hspace{0.17em}{w}_{m_1}^{\left(1\right)}-y_{m_2}^{\left(1\right)}\left(t-\mathrm{\Delta }t\right)\hspace{0.17em}{w}_{m_2}^{\left(1\right)}\\ {\widehat{y}}_{m_2}^{\left(1\right)}\left(t\right)=y_{m_1}^{\left(1\right)}\left(t-\mathrm{\Delta }t\right)\hspace{0.17em}{w}_{m_2}^{\left(1\right)}+y_{m_2}^{\left(1\right)}\left(t-\mathrm{\Delta }t\right)\hspace{0.17em}{w}_{m_1}^{\left(1\right)}\end{array}\hspace{0.17em}\left(\text{predicted}\hspace{0.17em}\text{responses}\right),$$

$$\begin{array}{l}{w}_{m_1}^{\left(1\right)}=\cos \left(2\pi {\omega }_m^{\left(1\right)}\mathrm{\Delta }t\right)\\ w_{m_2}^{\left(1\right)}=\sin \left(2\pi {\omega }_m^{\left(1\right)}\mathrm{\Delta }t\right)\end{array}\hspace{0.17em}\left(\text{temporal}\hspace{0.17em}\text{weights}\right).$$

The form of this optimization criterion is borrowed from signal processing (30). The values of y_m1 and y_m2 are the responses of a population of neurons that share the same input y⁽⁰⁾. The neurons are arranged in pairs (subscripts 1 and 2 with the same value for subscript m). As above, the neural responses are computed dynamically to minimize this energy function over time (SI Appendix). The values of ŷ_m1 and ŷ_m2 are the corresponding predictions of the responses from the previous time step (Δt is a discrete time step). I set the priors by hand in the examples above (Figs. 3 and 4), but here they are instead computed recursively. Specifically, they are computed (Eq. 3, second and third lines) as weighted sums of the responses from the previous time step with temporal weights w_m1 and w_m2 (a pair of numbers for each m). The temporal weights confer a 90° phase shift (sine and cosine; Eq. 3, fourth and fifth lines) between the responses of the two neurons in the pair. Different pairs of neurons (indexed by subscript m) have different dynamics (different temporal frequencies), controlled by the value of ω_m.

Table 2.

Notation for Eq. 3

Symbol	Description
$y_m^{\left(1\right)}\left(t\right)$	Responses over time of the $m$th pair of neurons, where $m$ specifies to the predictive frequency
$y^{\left(0\right)}\left(t\right)$	Input over time
${\widehat{y}}_m^{\left(1\right)}\left(t\right)$	Predicted responses for the $m$th pair of neurons
$w_m^{\left(1\right)}$	Temporal weights (a pair of numbers that depend on ${\omega }_m$) for the $m$th pair of neurons
${\omega }_m^{\left(1\right)}$	Predictive frequency (a constant) for the $m$th pair of neurons
$\lambda \left(t\right)$	State parameter

A one-layer network was constructed to follow an input for past time, but to predict for future time (Fig. 6, see SI Appendix for details). The input was a periodic time series, a sum of sinusoids, until t = 0 and then nonexistent for t > 0 (Fig. 6A, top panel). The network was constructed with five pairs of neurons, each pair corresponding to a different temporal frequency (Fig. 6B, blue and green curves). The output of the network (Fig. 6A, bottom panel) was computed by summing the responses of these 10 neurons across the five temporal frequencies (i.e., the blue curve in the bottom panel of Fig. 6A is the sum of the blue curves in Fig. 6B, and likewise for the green curves). The output (Fig. 6A, bottom panel, blue curve) followed the input (Fig. 6A, top panel) for past time because the state parameter λ was set to a relatively large value (λ = 0.1 for t ≤ 0). The network predicted forward in time (Fig. 6A, bottom panel), based on the current and past responses, because λ was set to a relatively small value (λ = 0.01 for t > 0).

Fig. 6.

Prediction. (A) Input and output. Top panel, input is a sum of two sinusoids for past time (t ≤ 0) and nonexistent for future time (t > 0). Bottom panel, output. Blue curve, sum of the blue curves in B. Green curve, sum of the blue curves in B. State: λ = 0.1 for t ≤ 0 and λ = 0.01 for t > 0. (B) Responses of each individual neuron. Different panels correspond to predictive basis functions with different temporal frequencies (ω_m). Blue and green curves in each panel, responses of pairs of neurons with the same ω_m but with temporal phases offset by 90°. Red curve in each panel, square root of the sum of the squares of the blue and green curves. (C) Impulse response functions. State: λ = 0.1. Predictive basis function temporal frequency: ω_m = 4 Hz. Blue, green, and red curves, same convention as in B. (D) Impulse response functions for same pair of neurons as in C but different state: λ = 0.01. Time step: Δt = 10 ms for all four panels. See SI Appendix for details.

For a fixed value of λ, each pair of neurons acts like a shift-invariant linear system (i.e., a recursive linear filter). The predicted responses can be computed recursively, but they can also be expressed as a sum of basis functions that I call the “predictive basis functions.” The predictive basis functions (damped oscillators of various temporal frequencies) are the impulse response functions of these shift-invariant linear systems, each corresponding to a pair of neurons (indexed by m). Given the responses of a pair of neurons at only one instant in time, the predicted responses over time are proportional to the predictive basis functions, scaled by the responses at that instant in time. Given the responses over time up to a current instant in time, the predicted responses can be expressed as a sum of scaled copies of the predictive basis functions. For example, when ω_m = 0, the predictive basis function is an exponential decay, the response y_m is a low-pass filtered (blurred over time) copy of the input y⁽⁰⁾, and the value of the state parameter λ determines the amount of blurring.

A change in state (λ = 0.1 versus λ = 0.01) corresponded to a change in neural response dynamics (Fig. 6 C and D). Different values of λ corresponded to linear filters with different temporal impulse response functions. During the first part of the simulation (λ = 0.1 for t ≤ 0), the temporal impulse response function was relatively brief (Fig. 6C) and the temporal frequency bandwidth was correspondingly broad. During the second part of the simulation (λ = 0.01 for t > 0), however, the temporal impulse response function was extended in time (Fig. 6D) and the temporal frequency bandwidth was relatively narrow.

As an example of multilayer prediction, a network was implemented that predicted periodic visual motion (Fig. 7). The energy function for this multilayer network can be expressed by combining Eqs. 1 and 3 (SI Appendix). The visual stimulus was a sinusoidal grating pattern that moved periodically rightward and leftward. A simplified model of retinal processing consisted of a temporal filter at each spatial location (Fig. 7A). The output of this temporal filter at each spatial location served as the input to the network (Fig. 7C). Layer 1 of the network was a simplified model of the LGN, layer 2 was a simplified model of direction-selective V1 simple cells (Fig. 7B), and layer 3 was a simplified model of direction-selective V1 complex cells. There were two channels in layer 3, responding preferentially to leftward and rightward motion. The layer 3 responses modulated over time with the periodic motion (Fig. 7E), and they predicted that the modulation would continue. This modulation of the layer 3 responses fed back through layer 2 to layer 1 and constrained the predicted responses in layer 1 (Fig. 7D).

Fig. 7.

Multilayer prediction of periodic motion. (A) Impulse response of retinal temporal filters. (B) Spatial weighting functions of layer 2 neurons. (C) Input to the network. Space–time responses of the retinal temporal filters, shown for one dimension of space over time, in response to periodic motion for t ≤ 0. (D) Layer 1 responses. Responses followed the input for t ≤ 0 and predicted continued periodic motion for t > 0. (E) Layer 3 responses modulated over time with the periodic motion of the stimulus. Blue, green, and red curves, same convention as in Fig. 6. Blue curves in left-hand panels (0 Hz) are identical to, and hidden by, the red curves. State for t ≤ 0: λ = (0.9, 0.9, 0.9) and α = (1, 1, 1), for layers 1, 2, and 3, respectively. State for t > 0: λ = (0.9, 0.9, 0.001) and α = (0.001, 0.01, 1). Time step: Δt = 10 ms. See SI Appendix for details.

Discussion

This paper outlines a first step toward an empirically testable computational framework for cortical function, in which neural responses depend on a combination of feedforward drive (bottom-up input from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). Information processing is continuous and dynamic, and it predicts forward in time (or combines sensory information with different latencies). Noise serves to explore different possible interpretations (a form of stochastic optimization). Special cases of the theory approximate Bayesian inference/estimation in which the neural responses encode an implicit representation of a posterior probability distribution.

The theory is related to previous research in computational neuroscience and artificial intelligence. In some states, neural responses are dominated by the feedforward drive and the theory is identical to conventional feedforward models, thereby preserving all of the desirable features of those models. Specifically, with λ = 1 and with appropriate choices of weights, the theory is identical to convolutional neural nets used in artificial intelligence systems for object recognition (e.g., refs. 51 and 52), and to conventional hierarchical models of visual perception (e.g., refs. 27 and 53). In other states, the theory is a generative model (e.g., refs. 54–56) that constructs a sensory representation (e.g., in layer 1 of the example networks in this paper) from an abstract representation (e.g., in layer 3) via feedback. In still other states, the computational framework combines prior expectation with sensory input, and it explores different possible perceptual interpretations of ambiguous sensory inputs, akin to models based on Bayesian inference (2–5, 47–49). The noise-driven process of exploration was motivated by stochastic optimization algorithms (57) and is similar to models of sensory neuroscience that draw samples from an underlying probability distribution over possible percepts (9, 10, 39, 43). This sampling idea has been proposed as an alternative to probabilistic population codes (4) for representing uncertainty in neural systems. However, I see these two ideas as complementary, not mutually exclusive. Neural responses evolve dynamically over time in my networks, in part because of noise-driven exploration, while implicitly encoding a posterior probability distribution. I hypothesize that this noise-driven process of exploration is the essence of creativity.

Prediction Versus Predictive Coding.

Predictive coding theories of sensory and perceptual processing have been developed to “explain away” the sensory input (e.g., refs. 19, 20, and 58–61). These theories posit two functionally distinct subpopulations of neurons, one representing predictions and the other representing prediction errors. Prediction errors are propagated forward to the next layer in the hierarchy and predictions are transmitted via feedback to the previous layer. The idea is to account for the incoming sensory signal by means of a matching top-down prediction, so that less prediction error propagates up the hierarchy. However, most of these models do not posit how the brain predicts over time (i.e., they do not extrapolate forward in time—see below).

The feedforward and feedback drive in the current theory are analogous to those in the predictive coding models, but the variables are flipped. The representation is propagated forward and the errors are propagated backward. This is more in line with neurophysiological and psychophysical phenomena than predictive coding models. First, neurons exhibit sustained activity to a predictable visual stimulus (e.g., ref. 62). According to predictive coding theories, the forward-propagating responses correspond to the prediction error, which should rapidly decay to zero for a predictable stimulus. Neural activity typically decreases over time due to adaptation, but the responses do not go to zero, that is, they are not “explained away.” Second, imagining a familiar image evokes a sensory representation in visual cortex that is reconstructed from memory (e.g., ref. 29), not explained away.

Moreover, the conceptual approach in predictive coding theories is fundamentally different from the current theory. Predictive coding theories start with a generative model that describes how characteristics of the environment produce sensory inputs. Perception is presumed to perform the inverse mapping, from sensory inputs to characteristics of the environment. The current theory is built the other way around (“synthesis-by-analysis”). I start with a feedforward cascade of signal processing operations, following the success of both feedforward models of sensory neuroscience and feedforward artificial neural nets. The corresponding generative model is the inverse of this feedforward processing model, which can be computed by gradient descent with respect to the input (55, 63). The energy function in the current theory combines a feedforward processing model and the corresponding generative model, so that it can run bottom-up (feedforward signal processing), top-down (generative), or a combination of the two.

There is a paucity of theories for how the brain performs perceptual predictions over time and/or combines sensory information with different latencies in the past (19–23). Most of the so-called predictive coding models (cited above) do not posit how the brain predicts over time. The predictive coding models that do so (19, 20, 22), as discussed above, are inconsistent with empirical phenomena. Other theories that perform predictions over time are neurally inspired implementations of a Kalman filter (21) or a Markov chain (23).

The current theory posits a different process for how the brain might predict over time. It relies on recursive computation similar to a Kalman filter, that is, the predictive basis functions serve the same role as the dynamical system model in a Kalman filter. Also like a Kalman filter, the neural responses in the current theory implicitly represent both estimates and uncertainties over time. However, unlike a Kalman filter, this computational framework comprises processing at multiple temporal scales, with different predictive frequencies at each level of the hierarchy. Multiple temporal scales of processing, across brain areas, have been proposed theoretically (22, 50, 64) and observed empirically (e.g., refs. 65–67). I hypothesize that this hierarchy of timescales is determined by the temporal weights (that specify the temporal frequencies of the predictive basis functions); neurons with temporal weights corresponding to lower temporal frequencies accumulate information over a longer time period in the past and are capable of predicting forward in time over a correspondingly longer timescale.

There is some controversy about whether sensory systems perform prediction versus what has been called “postdiction” in which sensory information acquired at different latencies (all in the past) is used to construct a percept of the past (68). However, there is no distinction between the two in the current theory; both involve extrapolating over time. An intriguing hypothesis is that sensory awareness is the brain’s prediction of the present (e.g., ref. 16).

Learning.

Most artificial neural nets rely on supervised learning. In computer vision, for example, an image is presented to a neural net, which attempts to categorize the image as one of a fixed set of possibilities. The network produces an output (e.g., at the top of the hierarchy in a deep convolutional neural net), which is compared with a desired output. The desired output is specified a priori (e.g., by hand-labeling the identity of an object in an image). The difference between the output and desired output is used to adjust the weights via “backpropagation” (gradient descent on the weights in every layer with respect to the error in the output). This requires a large library of images, each of which is prelabeled with a category.

The example networks presented in this paper were hand-tuned (ad hoc), but they could instead be learned using an unsupervised learning algorithm that extracts regularities in the inputs without labels. The neural responses are modeled as dynamical processes that compute a weighted sum of feedforward drive, feedback drive, and prior drive (Eq. 2). The prior drive, in turn, depends on a weighted sum of previous responses over time (Eq. 3). These spatial and temporal weights can be learned based on prediction errors for a time-varying input (e.g., video). Each neuron in the network produces a prediction for what its response will be later in time, and the weights are adjusted to minimize the difference between these predicted responses and the actual responses that occur later in time. This is similar to what has been called “target propagation” (as opposed to the more familiar backpropagation) in the neural net literature (69–71). Periods of inference, exploration, and prediction, during which the neural responses evolve dynamically (e.g., via gradient descent on the responses as in Eq. 2) alternate (by changing the state of the network) with periods of learning during which the weights are updated (via gradient descent on the weights). Alternation between inference and learning, and learning based on different timescales at each level of the hierarchy, are each reminiscent of previous unsupervised learning algorithms (50, 56).

A challenge for backpropagation-based learning is that it is nonlocal, requiring the weights in one channel/layer to be updated based on errors in other channels/layers. Nonlocality is considered by many to be biologically implausible, although there are some proposals for how to implement backpropagation with only local, biologically plausible weight updates (72–74). The idea here is to circumvent this problem entirely by updating each neuron’s (spatial and temporal) weights locally, based only on that neuron’s prediction errors.

The priors can also be learned (SI Appendix).

Brain States, Neuromodulators, and Oscillatory Activity.

The values of the state parameters (α and λ) might be controlled by acetylcholine (ACh), given the evidence that ACh plays a role in modulating the trade-off between bottom-up sensory input versus top-down signals related to expectancy and uncertainty (SI Appendix). In addition, there is considerable evidence that attention modulates the gain of neural responses, suggesting that α might be controlled also by attention (SI Appendix). Neuromodulators might also control changes in state to enable learning (SI Appendix). According to the theory, exploration depends on neural response variability, which might be controlled (at least in part) by noradrenaline, and/or by oscillations in brain activity (SI Appendix).

Empirical Relevance.

There are a number of variations of the computational framework, depending on the network architecture, output nonlinearity, and optimization algorithm (SI Appendix). Some of the variants or some of the components of the computational framework might be completely wrong, whereas others are less wrong; that is, falsifying just one variant or one component would not render the entire computational framework worthless. The simulation examples presented in this paper were designed to illustrate the principles of the theory, not to model the responses of any particular neurons or neural systems. It remains to be seen whether these principles can be applied to fit neurophysiological and/or behavioral data, and/or applied in computer vision or artificial intelligence systems. In the meantime, there are some general principles of the theory that are empirically relevant and/or that motivate experiments. Some examples are as follows:

• i)According to the theory, prediction is performed recursively (Eq. 3; Fig. 2D, dashed line) with pairs of neurons that have identical response properties except that they respond with different temporal phases (e.g., pairs of neurons with temporal phases offset by 90°, although any two or more phases would suffice). There is evidence that adjacent pairs of simple cells in V1 have receptive fields with 90° or 180° shifts in spatial phase (e.g., ref. 75), but temporal-phase relationships between nearby neurons have not been reported.
• ii)The theory posits neurons with similar preferences for sensory stimuli, but with different dynamics, which together make up a basis set for predicting forward in time.
• iii)Changing the state (the value of λ) corresponds to a change in neural response dynamics (Fig. 6 C and D). Such changes in state are hypothesized to be controlled by fluctuations in ACh (SI Appendix).
• iv)Alternations in perceptual state (e.g., for bistable perceptual phenomena) depend on neural response reliability. Changes in neural response reliability are hypothesized to be driven by fluctuations in noradrenaline and by oscillations in brain activity (SI Appendix).
• v)Functional connectivity between pairs of neurons is hypothesized to depend on brain state. When in a bottom-up sensory processing state, the feedback connections will appear to be weak. When in a top-down processing state, the feedback connections will be strong but the feedforward connections will appear weak. Functional connectivity is also hypothesized to depend on context. Take, for example, my simple XOR-like inference network (Fig. 3), and imagine an experiment to perturb the neural responses by injecting current in either the first of the layer 1 neurons (Fig. 3A, blue circle) or the first of the layer 2 neurons (Fig. 3A, purple circle), with state parameters that enable a combination of bottom-up and top-down processing. With input y⁽⁰⁾ = (0.1, 0, 0, 0) and prior ŷ = 0 for all neurons in the network, positive perturbations of either the layer 1 neuron or the layer 2 neuron causes the other neuron to respond more. If the input is instead y⁽⁰⁾ = (0.1, 1, 0, 0), then positive perturbations of either the layer 1 neuron or the layer 2 neuron causes the other neuron to respond less. Additionally, if the input is y⁽⁰⁾ = (1, 1, 0, 0), then shutting down the layer 2 neuron causes a different neuron in layer 1 (Fig. 3A, green circle) to respond more. Given how complicated this is for such a simple network, I worry about how to interpret the results of optogenetic experiments in the absence of predictions from specific computational models.

Computational Theory.

The current theory is intended, following the terminology of David Marr (76), to characterize cortical function at a computational level of abstraction (what the brain might be optimizing, e.g., Eqs. 1 and 3), and at an algorithmic level of abstraction (signal-processing computations to perform the optimization, e.g., Eq. 2), not in terms of the underlying circuit, cellular, molecular, and biophysical mechanisms. There are a number of variations of the optimization criterion, depending on the architecture of the network and choices for the nonlinearities. For each choice of optimization criterion, there are also a number of possible optimization algorithms (for which Eq. 2 is only one example). For any given choice of network architecture, optimization criterion, and optimization algorithm, there are a variety of mechanisms that might implement the computations embodied in the theory.

For example, I developed the normalization model 25 y ago to explain stimulus-evoked responses of V1 neurons (31). The model has since been applied to explain physiological measurements of neural activity in a wide variety of neural systems, and behavioral/perceptual analogs of those physiological phenomena (32) (SI Appendix). However, only recently has there been progress in elucidating the underlying mechanisms, which have been found to be different in different neural systems (SI Appendix).

Computational theory is an intermediate level of abstraction between the underlying mechanisms, on the one hand, and physiology and behavior, on the other (77). The field of neuroscience might benefit from the recognition, in other fields of science, that reductionism is inherently limited, and that there are fundamental organizing principles at intermediate levels (e.g., ref. 78). Computation might be such a critical intermediate level for characterizing brain function. Consequently, it may be extremely useful to identify abnormal computations in individuals with particular psychiatric and neurodevelopmental disorders. For example, deficits in normalization (79), deficits in prediction (80), dysfunctional Bayesian inference (81), and uncontrolled neural response variability (82, 83) have each been hypothesized to underlie autism.